jev-compactor
FeaturedDesign and tune a Jev Compactor session. Use when editing session.json, writing task vocabularies, or looking for settings where needle recall holds while the reduction is large.
Install
Quality Score: 93/100
Skill Content
Details
- Author
- autonomous-ai
- Repository
- autonomous-ai/openharness
- Created
- 1 months ago
- Last Updated
- today
- Language
- C
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
building-with-jev
Write, compose, integrate, and improve programs that call Jev, TypeSafe's System One judgment model.
jev
Set up Jev via OpenRouter, native TypeSafe or explicitly chosen simulation; build custom decision integrations and handle agent checkpoints, tool/model routing and context-retention decisions. Supply relevant context and batch independent questions. Focused collection skills cover bulk labels, evidence retrieval, evaluation, UI and simulations.
jev
Use the Jev decision model (TypeSafe System One, via OpenRouter) for bounded decisions inside coding workflows — routing, triage, classification, gating, rubric grading, ranking, and reducing large data before it reaches the main model. Use when a decision has a fixed set of possible answers, when data is too large or too noisy to put in context, when the same judgement must be made many times, or when an irreversible action needs a cheap safety check. Jev emits no text: never use it for prose, code generation, summaries or reasoning. Triggers: "classify", "categorize", "which of these", "route", "triage", "gate", "should we", "rank", "prioritize", "grade", "too many logs", "reduce the data", "save tokens", "batch decisions", "is it safe to". Advisory, never an authorization boundary: Jev can be wrong, manipulated or overconfident, so do not map a returned label straight to an irreversible or destructive action without your own deterministic check.